Recently, vehicle roll stability control (RSC) schemes, i.e., U.S. Pat. No. 6,324,446, have been proposed to address the issue of friction-induced rollovers. RSC system includes a variety of sensors sensing vehicle states and a controller that controls a distributed brake pressure to reduce a tire force so the net moment of the vehicle is counter to the roll direction.
During an event causing the vehicle to roll, the vehicle body is subject to a roll moment due to the coupling of the lateral tire force and the lateral acceleration applied to the center of gravity of vehicle body. This roll moment causes suspension height variation, which in turn results in a vehicle relative roll angle (also called chassis roll angle or suspension roll angle). The relative roll angle is an important variable that is used as an input to the activation criteria and to construct the feedback brake pressure command, since it captures the relative roll between the vehicle body and the axle. The sum of such a chassis roll angle and the roll angle between wheel axle and the road surface (called wheel departure angle) provides the roll angle between the vehicle body and the average road surface, which is one of the important variables feeding back to the roll stability control module.
The chassis roll angle can be calculated as in U.S. Pat. No. 6,556,908 using the lateral acceleration of the center of gravity of the vehicle body, the roll angular acceleration, and the roll angular velocity, together with vehicle-specific parameters such as the sprung mass, the vehicle body roll moment of inertia, the roll stiffness and damping ratio of the suspensions and the anti-roll-bars, and the distance between the center of gravity of the vehicle body and the floor of the vehicle body. The disclosure of U.S. Pat. No. 6,556,908 is hereby incorporated by reference.
One problem with using these parameters in the computation the afore-mentioned relative roll angle is that they may vary with the vehicle operating conditions. For example, a 150 pound roof loading for a typical SUV with a curb weight of 5000 pounds may cause more than 30% error in relative roll angle calculations if computed assuming no roof load. From the vehicle mass point of view, although a 150 pound roof loading accounts for only a 3% mass variation over the vehicle curb weight, it could account for a 30% error in the chassis roll computation, which is ten times larger. If the above parameters are fixed at certain nominal values in the RSC system, it is conceivable that optimal control performance may not be achieved under a different loading condition. For example, if the relative roll angle is computed with nominal vehicle loading condition assumptions, without considering roof loading, the relative roll angle may be under estimated for vehicles with roof loadings, which results in a reduced control. That is, the control system may not be as effective as desired. On the other hand, if the relative roll angle is computed with maximum roof loading, it may be over estimated for vehicles without roof loadings causing unintended control. That is, the control system may become too sensitive or intrusive. Therefore, in order to improve the overall performance of the RSC system, it may be desirable to estimate and update the vehicle parameters periodically or adaptively adjust in real time based on the detected vehicle loading.
Certain schemes for obtaining vehicle parameters have been disclosed. For example, in U.S. Pat. No. 4,548,079, a method is disclosed for determining vehicle mass directly using engine output torque and vehicle acceleration. Similarly, in U.S. Pat. No. 5,490,063, push force is determined from the driveline torque and gear ratio to obtain vehicle mass. In U.S. Pat. No. 6,167,357, instead of calculating vehicle mass directly, a recursive least square (RLS) algorithm is proposed to estimate both vehicle mass and aerodynamic coefficient online. The latter method is considered to be more reliable since it recursively adjusts for estimation error of the previous estimates. Furthermore, the use of vehicle acceleration, which is usually very noisy, is avoided. Notice that the mass estimation schemes proposed in the above-cited patents may not accurately indicate changes to parameters that impact the roll dynamics of the vehicle. For example, a 150 pound roof loading on a 5000. pound SUV, i.e., 3% mass change, might be undetectable in the above schemes due to the potential error in the engine torque, which usually is much larger than 3%. Other error sources include the road grade, the tire rolling radius change due to tire pressure drop and due to the vehicle loading variations and the vehicle drag.
The above schemes focus mainly on large mass variations which may have significant influences on the vehicle longitudinal dynamics and vehicle fuel consumption. They do not differentiate whether the vehicle mass change is due to a floor loading or due to a roof loading. However, the roof loading causes much more significant roll motion parameter changes than the same amount of floor loading does. That is, there is a need to detect not only the amount of loading (maybe small), but also the location of the loading (the vertical and longitudinal distance of the loading with respect to the vehicle floor or the center of gravity of the vehicle body, for example).
That is, the prior art does not address vehicle inertia and mass properties with respect to the vehicle body roll and lateral dynamics. The estimation methodologies suggested in the literature consider the vehicle longitudinal dynamics and are not appropriate for an RSC system where the lateral and roll dynamics are more important than longitudinal dynamics.
Furthermore, the other parameters that effect vehicle body roll and lateral dynamics, such as the roll stiffness and damping in the suspension, the total center of gravity height of the vehicle body with respect to the vehicle floor and the roll moment of inertia, have not been estimated and/or considered in the prior art.
In the parent application, a method to estimate coefficients (i.e., roll gradient, roll acceleration coefficient, roll rate coefficient, roll moment of inertia, mass, CG height) related to vehicle loading conditions by analyzing data from the sensors on the vehicle was set forth. These estimates become more robust as the data is averaged over the “long term” or a large variety of the driving conditions; the more data that is averaged, the more robust the estimates can be. However, this averaging process needs to be reset in cases where there is an indication of potential load changes. For example, each time the vehicle stops for long period of time, there is the potential that the vehicle loading has changed. In reality the vehicle loading may not change, however, with no qualitative indication that the load did or did not change, the conservative approach is to always reset the averaging process. Such a algorithm of determining when a potential loading change occurs, used in determining if resetting the averaging process is necessary, is called qualitative load change determination. Therefore, there is a need for a technique to determine qualitatively when a load has potentially changed so that vehicle parameters may be refined and averaged quantitatively in response to the qualitatively changed load so as to improve the robustness of estimated parameters and to improve a vehicle control system relating to roll stability control functions.